HELP: HyperNode Expansion and Logical Path-Guided Evidence Localization for Accurate and Efficient GraphRAG

TL;DR

This paper proposes HyperNode Expansion and Logical Path-Guided Evidence Localization strategies for GraphRAG (HELP), a novel framework designed to balance accuracy with practical efficiency through two core strategies: HyperNode Expansion and Logical Path-Guided Evidence Localization.

Abstract

Large Language Models (LLMs) often struggle with inherent knowledge boundaries and hallucinations, limiting their reliability in knowledge-intensive tasks. While Retrieval-Augmented Generation (RAG) mitigates these issues, it frequently overlooks structural interdependencies essential for multi-hop reasoning. Graph-based RAG approaches attempt to bridge this gap, yet they typically face trade-offs between accuracy and efficiency due to challenges such as costly graph traversals and semantic noise in LLM-generated summaries. In this paper, we propose HyperNode Expansion and Logical Path-Guided Evidence Localization strategies for GraphRAG (HELP), a novel framework designed to balance accuracy with practical efficiency through two core strategies: 1) HyperNode Expansion, which iteratively chains knowledge triplets into coherent reasoning paths abstracted as HyperNodes to capture complex structural dependencies and ensure retrieval accuracy; and 2) Logical Path-Guided Evidence Localization, which leverages precomputed graph-text correlations to map these paths directly to the corpus for superior efficiency. HELP avoids expensive random walks and semantic distortion, preserving knowledge integrity while drastically reducing retrieval latency. Extensive experiments demonstrate that HELP achieves competitive performance across multiple simple and multi-hop QA benchmarks and up to a 28.8$\times$ speedup over leading Graph-based RAG baselines.

Figures (4)

• Figure 1: Overview of the HELP framework. The workflow consists of three stages: (I) Knowledge Graph Construction, utilizing OpenIE to build the Triple-to-Passage Index; (II) Iterative HyperNode Expansion, which iteratively chains triples as HyperNodes while pruning irrelevant ones to maintain reasoning coherence; and (III) Logical Path-Guided Evidence Localization, where HyperNodes are grounded back to the original passages for precise evidence retrieval via a hybrid mechanism.
• Figure 2: Retrieval efficiency on PopQA (Simple QA) and 2Wiki (Multi-Hop QA). Absolute retrieval time (in seconds) for processing 1,000 queries are annotated above the bars, highlighting that HELP reduces the total retrieval latency to under 90 seconds.
• Figure 3: Experimental analysis of expansion hops $N$ on HotpotQA dataset. The bars indicate retrieval time (left axis), while the line tracks the QA F1 score (right axis).
• Figure 4: Hyperparameter sensitivity analysis of the Initial Triple Size ($n$) and Hypernode Beam Size ($k$) on MuSiQue dataset. The heatmap reports the F1 scores (%), demonstrating the impact of varying the seed set size and pruning threshold.